ArticleBMC medical genomics2018
Identification of glioblastoma gene prognosis modules based on weighted gene co-expression network analysis.
Article in BMC medical genomics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 38 papers, 1 of them a synthesis that pooled it.
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Who cites it
38 citing papers in PubMed, 1 synthesis or guideline pooled it.
- mRNA markers for survival prediction in glioblastoma multiforme patients: a systematic review with bioinformatic analyses.BMC cancer · 2024Pooled it
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- Development of a web-based tool for estimating individualized survival curves in glioblastoma using clinical, mRNA, and tumor microenvironment features with fusion techniques.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2025Article
- PTPRN promotes glioma cell proliferation, migration, and invasion by regulating the EMT pathway.International journal of clinical and experimental pathology · 2025Article
- Identifying cancer prognosis genes through causal learning.Briefings in bioinformatics · 2024Article
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- A transcriptome-wide analysis provides novel insights into how Metabacillus indicus promotes coral larvae metamorphosis and settlement.BMC genomics · 2024Article
- Identification of Prognostic Genes in Gliomas Based on Increased Microenvironment Stiffness.Cancers · 2022Article
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- Data mining and mathematical models in cancer prognosis and prediction.Medical review (2021) · 2022Review
- The barleyFrontiers in genetics · 2022Article
- Comprehensive analysis for clarifying transcriptomics landscapes of spread through air spaces in lung adenocarcinoma.Frontiers in genetics · 2022Article
- MicroRNA-744-5p inhibits glioblastoma malignancy by suppressing replication factor C subunit 2.Oncology letters · 2021Article
- Knockdown of CLN5 inhibits the tumorigenic properties of glioblastoma cells via the Akt/mTOR signaling pathway.Oncology letters · 2021Article
- The Role of Network Science in Glioblastoma.Cancers · 2021Review
- Identification of novel candidate biomarkers for pancreatic adenocarcinoma based on TCGA cohort.Aging · 2021Article
- 6-lncRNA Assessment Model for Monitoring and Prognosis of HER2-Positive Breast Cancer: Based on Transcriptome Data.Pathology oncology research : POR · 2021Article
- Prognostic Prediction Model for Glioblastoma: A Metabolic Gene Signature and Independent External Validation.Journal of Cancer · 2021Article
- Overexpression of PTPRN Promotes Metastasis of Lung Adenocarcinoma and Suppresses NK Cell Cytotoxicity.Frontiers in cell and developmental biology · 2021Article
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9 authors.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundGlioblastoma multiforme, the most prevalent and aggressive brain tumour, has a poor prognosis. The molecular mechanisms underlying gliomagenesis remain poorly understood. Therefore, molecular research, including various markers, is necessary to understand the occurrence and development of glioma.
methodWeighted gene co-expression network analysis (WGCNA) was performed to construct a gene co-expression network in TCGA glioblastoma samples. Gene ontology (GO) and pathway-enrichment analysis were used to identify significance of gene modules. Cox proportional hazards regression model was used to predict outcome of glioblastoma patients.
resultsWe performed weighted gene co-expression network analysis (WGCNA) and identified a gene module (yellow module) related to the survival time of TCGA glioblastoma samples. Then, 228 hub genes were calculated based on gene significance (GS) and module significance (MS). Four genes (OSMR + SOX21 + MED10 + PTPRN) were selected to construct a Cox proportional hazards regression model with high accuracy (AUC = 0.905). The prognostic value of the Cox proportional hazards regression model was also confirmed in GSE16011 dataset (GBM: n = 156).
conclusionWe developed a promising mRNA signature for estimating overall survival in glioblastoma patients.
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